Deep Dive into Softmax and Cross Entropy | PyTorch Explained

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Welcome to another exciting tutorial on deep learning with PyTorch! In this video, we dive deep into the concepts of Softmax and Cross Entropy, crucial components in the realm of neural networks and machine learning. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT PyTorch for Beginners Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4KzVIok0mWdp-zigfdOKZI- PyTorch Data Transforms: https://www.youtube.com/watch?v=A_g6vsW8jtk&ab_channel=RyanNolanData PyTorch Gradients: https://www.youtube.com/watch?v=LWnXFfNVjq0&feature=youtu.be PyToch Multiclass Classification: https://www.youtube.com/watch?v=iWdVXAwurXs&ab_channel=RyanNolanData In this deep learning tutorial, we break down softmax and cross entropy loss in PyTorch, two essential concepts for classification problems. This video is part of my comprehensive PyTorch series, perfect for anyone learning deep learning and neural networks. First, I explain what softmax does—it transforms logits into probabilities that sum to one, making it crucial for multi-class classification. Then we dive into cross entropy loss (also called log loss), which measures how well your model's predictions match the actual targets. The key insight is that PyTorch's cross entropy loss function automatically applies softmax internally, so you don't need to do it manually. We walk through two complete coding examples in Google Colab. In the first example, I show you the manual way of calculating cross entropy loss using softmax probabilities, then demonstrate the much simpler approach using nn.CrossEntropyLoss(). The second example uses random data to illustrate what happens when predictions are poor versus accurate. I also cover what constitutes good cross entropy scores—aim for under 0.3 to 0.4, and if you're above 2.0, something is likely broken in your code. By the end of this tutorial, you'll understand exactly how to implement softmax and cross entropy loss in your PyTorch models and interpret the results for better model performance. TIMESTAMPS 00:00 Introduction to Softmax and Cross Entropy Loss 00:57 Understanding Softmax and Cross Entropy 01:30 Setting Up the Code in Google Colab 02:20 Defining Logits and Targets 03:05 Implementing Softmax in PyTorch 04:00 Converting Logits to Probabilities 04:45 Manual Cross Entropy Loss Calculation 06:15 The Easy Way: Using nn.CrossEntropyLoss 07:30 Understanding Good Loss Scores 08:15 Example 2: Random Data Classification 10:00 Why Classification Results Matter OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.